# -*- coding: utf-8 -*-
from datetime import timedelta
from jms.ods.mysql.offline_complaint import jms_ods__offline_complaint
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator

jms_dwd__dwd_offline_complaint_dt = SparkSqlOperator(
    task_id='jms_dwd__dwd_offline_complaint_dt',
    task_concurrency=1,
    pool_slots=1,
    execution_timeout=timedelta(minutes=30),
    email=['rongguangfan@jtexpress.com','yl_bigdata@yl-scm.com'],
    master='yarn',
    name='jms_dwd__dwd_offline_complaint_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dwd/January/dwd_offline_complaint_dt/execute.hql',
    driver_memory='3G',
    driver_cores=2,
    executor_cores=2,
    executor_memory='3G',
    num_executors=5,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 8,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 80,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.hadoop.hive.exec.dynamic.partition.mode': 'true',
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 61,  # 每天生成 35 个分区
              'hive.exec.max.dynamic.partitions.pernode': 61,  # 每天生成 35 个分区
              },
)

jms_dwd__dwd_offline_complaint_dt << [
    jms_ods__offline_complaint,
]
